Canary 1B

May 9, 2026 · View on GitHub

NVIDIA's nvidia/canary-1b ported to transcribe.cpp. A 1B-parameter multitask AED with a 24-layer FastConformer encoder and a 24-layer Transformer decoder — the original canary release.

License: CC-BY-NC-4.0 (non-commercial only). This is the only canary variant under a non-commercial license. Every shipped GGUF carries general.license: CC-BY-NC-4.0 in its KV metadata so downstream tooling can detect this without re-reading the model card.

What it's for

Offline multilingual speech-to-text and translation. The model takes a 16 kHz mono WAV and produces a transcript. Supports:

ASR in English, German, Spanish, and French.
Translation between supported pairs.

See NVIDIA's model card for training data, intended use, and upstream evaluation methodology.

Ported from upstream commit 1698acf, pinned 2026-05-08.

Download

Quantization	Download	Size	WER (LibriSpeech test-clean)
F32	canary-1b-F32.gguf	3.8 GB	1.55%
F16	canary-1b-F16.gguf	1.9 GB	1.55%
Q8_0	canary-1b-Q8_0.gguf	1.1 GB	1.55%
Q6_K	canary-1b-Q6_K.gguf	891 MB	1.57%
Q5_K_M	canary-1b-Q5_K_M.gguf	799 MB	1.57%
Q4_K_M	canary-1b-Q4_K_M.gguf	696 MB	1.55%

WER is measured on the full LibriSpeech test-clean split (2620 utterances) with greedy decoding and no external LM. F32 reference baseline: 1.55%. NVIDIA's self-reported number on the upstream model card is 1.48%. Likely this is due to differences in how we score WER based on the results we have from Canary 180m.

Quick Start

cmake -B build
cmake --build build

# ASR (English)
build/bin/transcribe-cli \
  -m models/canary-1b/canary-1b-Q8_0.gguf \
  -l en \
  samples/jfk.wav

# Translation (English audio → German text)
build/bin/transcribe-cli \
  -m models/canary-1b/canary-1b-Q8_0.gguf \
  --task translate \
  -l en --target-language de \
  samples/jfk.wav

If your audio is not already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

CLI flags specific to canary:

--pnc / --no-pnc — punctuation & capitalization (default on).
-l <code> — source language code (en, de, es, fr).
--task translate + --target-language <code> — switch to translation mode. canary-1 uses an explicit <|translate|> task token (the canary2 variants infer translate from src ≠ tgt instead).

Performance

Cells are wall-clock latency (mean over 3 iterations after 1 warmup), with speedup over realtime in parentheses. Units: ms below 1 s, s above (2 decimal places).

The 24-layer decoder makes this the slowest canary variant for decode-bound workloads — roughly 1.5× the wall time of canary-1b-flash on the same backend, and the GPU win over CPU is smaller here than on the *flash variants because the autoregressive decoder pass dominates and a batch-1 / single-token forward is too small to amortize Vulkan dispatch overhead.

Apple M4 Max

Backend	Sample	Q8_0	Q4_K_M
Metal	jfk (11.0s)	216.0 ms (50.9×)	198.1 ms (55.5×)
Metal	dots (35.3s)	1.03 s (34.2×)	937.2 ms (37.7×)
CPU	jfk (11.0s)	511.1 ms (21.5×)	426.2 ms (25.8×)
CPU	dots (35.3s)	2.05 s (17.3×)	1.72 s (20.6×)

macOS 26.4.1, transcribe.cpp 19b3b87.

AMD Ryzen 7 PRO 4750U

Backend	Sample	Q8_0	Q4_K_M
Vulkan	jfk (11.0s)	1.07 s (10.2×)	923.4 ms (11.9×)
Vulkan	dots (35.3s)	4.63 s (7.6×)	3.87 s (9.1×)
CPU	jfk (11.0s)	1.71 s (6.4×)	1.24 s (8.9×)
CPU	dots (35.3s)	7.59 s (4.7×)	5.97 s (5.9×)

Fedora Linux 43, transcribe.cpp 4d44530. Vulkan device: AMD Radeon Graphics (RADV RENOIR).

Benchmark reproduction:

uv run scripts/bench/run.py \
  --models canary-1b \
  --quants q8_0,q4_k_m \
  --samples jfk,dots \
  --backends metal,cpu,vulkan \
  --iters 3 --warmup 1 \
  --name canary-1b-publication

Numerical Validation

transcribe.cpp is validated tensor-by-tensor against NeMo on samples/jfk.wav. All checkpointed tensors fall within family tolerance and the F32 transcript matches the NeMo reference. Last validated at commit db53eda.

Field	Value
Reference	NeMo, `nvidia/canary-1b`
Dump script	`scripts/dump_reference_canary_nemo.py`
Manifest	`tests/golden/canary/canary-1b.manifest.json`
Tolerances	`tests/tolerances/canary.json`
Command	`uv run scripts/validate.py all --family canary --variant canary-1b`

For the full porting writeup, see docs/porting/families/canary.md.

Reproduction

Convert

uv run --project scripts/envs/canary \
  scripts/convert-canary.py nvidia/canary-1b --repo-id nvidia/canary-1b

Quantize

uv run scripts/quantize-all.py models/canary-1b/canary-1b-F32.gguf

Validate

uv run scripts/validate.py all --family canary --variant canary-1b